NeuroAI: The Computing Brain (15 EC)

Decode biological intelligence through computational models of neurons, neuroscience, and creative inquiry.

The NeuroAI minor is an educational experience for bachelor students of varied backgrounds, whereby students pursue a guided autodidactic approach to acquire knowledge and develop confidence about neuroscience and computational modeling of the brain. The NeuroAI minor has a strong focus on collaborative knowledge discovery of the foundations of modern approaches in NeuroAI. It is divided in two parts, each containing 15EC, both of which can be taken independently. The minor is designed with transdisciplinarity in mind and can be taken by virtually anyone with an interest in how the brain computes. The minor is organized around activities that foster a collaborative and transdisciplinary approach towards understanding the brain by combining theory, code, models, experimental science and philosophy.

The program runs in two parts , each worth 15 EC :

Part 1 – Foundations of NeuroAI introduces neural computation, modelling, and the conceptual roots of AI–brain analogies. In project based learning (PBL) settings, student groups will learn to simulate neurons and networks, interpret computational models, and create an explainer video on a chosen topic in NeuroAI.
Part 2 – Advanced NeuroAI & Reproduction focuses on open science and reproducibility. Students work in transdisciplinary groups to acquire the skills reproduce or extend an existing NeuroAI study, analyze real data, and present their findings at a final symposium.

Both parts combine coding, creativity, and collaboration, fostering a deep understanding of how intelligence can emerge from neural activity.

• Integrate perspectives from neuroscience, AI, and philosophy of mind.
• Create a multimedia explainer (video and live web document).
• Collaborate effectively in transdisciplinary teams.
• Evaluate scientific claims using data and reproducibility principles.
• Use and adapt computational models to explore neural and cognitive functions.
• Explain how neurons, networks and brains work according to models at levels of analysis.
• Reflect on your own learning process and scientific reasoning.

• This minor is open to all disciplines.
• No prior coding experience is required— enthusiasm and curiosity are.
• Attendance at workshops, symposia and coaching sessions is mandatory .
• Teaching takes place on campus at Erasmus MC , with hybrid collaboration through Canvas and Colab.
• Recommended background: English proficiency and interest in neuroscience, AI, or data science.

Teaching methods
Teaching is interactive and project-based. Weekly workshops , question storms , and explainer clinics link theory to hands-on modelling. Students participate in journal clubs , coding sprints , and peer reviews to deepen understanding and develop collaborative skills. Continuous feedback supports independent learning.

Teaching material

• Curated resources are shared via Canvas. All materials are digital and open-source: interactive notebooks, simulation scripts, scientific papers, and online atlases. Students work with Google Colab, GitHub, and multimedia tools to build explainers and reports. In a workshop, students build embodied robots with modular robotics (SamLabs).

Examination
Assessment is based on cumulative deliverables and a final product.
Part 1: individual explainer video, accompanying live document (referenced) and reflection log.
Part 2: team reproduction or extension of a published study, culminating in a poster, video pitch, and written report.
Peer and instructor feedback guide revisions. No written exams—performance is measured through understanding, creativity, and collaboration.

Composition final grade

• Final project deliverables – 60 %
• Collaboration and process quality – 20 %
• Weekly deliverables, questions and reflections - 20 %
• Grades reflect conceptual depth, reproducibility, and clarity of communication.